What should we decide before ordering?
What decision is the AI allowed to influence?
Private local AI applies this principle to local user or application, private data store and local inference host. Drafting a reply, finding a policy paragraph and switching a light carry different risks. We classify whether the output is advice, a suggestion, a reversible command or an action with material effect. The delivery timeline surveys local user or application before design approval, then tests local inference host during commissioning. For this service, the scope records how bounded connector connects to reviewed output, then verifies that relationship during commissioning or recovery testing.
Private local AI applies this principle to local compute and storage, model access policy and customer lawful use. Retrieval, tools and larger models may reduce some errors, but none turns generated text into verified truth. We design citations, confidence cues and human review around the actual consequence. Medical, legal, financial, employment, security and life-safety decisions require qualified owners outside the model. For this service, the scope records how downloads and support tools connects to local compute and storage, then verifies that relationship during commissioning or recovery testing.
Where may data travel and remain?
Private local AI applies this principle to model workload, memory and accelerator and offline requirement. It does not mean every connected feature is offline or that privacy appears automatically. Model downloads, telemetry, speech services, connectors, backups and support tools can cross that boundary. For this service, the scope records how connector risk connects to update ownership, then verifies that relationship during commissioning or recovery testing.
Private local AI applies this principle to model unavailable, disable connected action and use ordinary workflow. A knowledge assistant must not reveal a document merely because it indexed the words. Service accounts receive the narrow access needed for their task, secrets stay out of prompts and logs, and retention is set deliberately. The customer decides the lawful basis, staff policy and approved datasets. For this service, the scope records how restore known version connects to repeat evaluation set, then verifies that relationship during commissioning or recovery testing.
How is accuracy tested after launch?
Private local AI applies this principle to network-boundary check, latency sample and permission denial. Acceptance testing uses representative inputs, awkward phrasing, missing information, denied requests and known edge cases. The team records the expected outcome, model response, source citation, tool call and reviewer decision. Maintenance repeats the network-boundary check and latency sample checks after material changes, updates or reported faults. For this service, the scope records how offline test connects to versioned evaluation, then verifies that relationship during commissioning or recovery testing.
Private local AI applies this principle to local compute and storage, model access policy and customer lawful use. Version records and a small repeatable test set make drift visible after an update. There is always a non-AI route for important work: manual control, ordinary search, a queue for staff review or a disabled automation. We prefer a narrow assistant whose limits are obvious to a broad agent with vague authority. A lower-complexity alternative remains valid when downloads and support tools can be handled by the documented manual or existing-system route. For this service, the scope records how downloads and support tools connects to local compute and storage, then verifies that relationship during commissioning or recovery testing.
